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betavae.py
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betavae.py
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import os
import torch
import torch.nn as nn
import torch.nn.init as init
def kaiming_init(m):
if isinstance(m, (nn.Linear, nn.Conv2d)):
init.kaiming_normal_(m.weight)
if m.bias is not None:
m.bias.data.fill_(0)
elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
m.weight.data.fill_(1)
if m.bias is not None:
m.bias.data.fill_(0)
def reparametrize(mu, logvar):
std = logvar.div(2).exp()
eps = torch.Tensor(std.data.new(std.size()).normal_())
return mu + std*eps
class View(nn.Module):
def __init__(self, size):
super(View, self).__init__()
self.size = size
def forward(self, tensor):
return tensor.view(self.size)
class BetaVAE_H(nn.Module):
"""Model proposed in original beta-VAE paper(Higgins et al, ICLR, 2017)."""
def __init__(self, z_dim=10, nc=3):
super(BetaVAE_H, self).__init__()
self.z_dim = z_dim
self.nc = nc
self.encoder = nn.Sequential(
nn.Conv2d(nc, 32, 4, 2, 1), # B, 32, 64, 64
nn.ReLU(True),
nn.Conv2d(32, 32, 4, 2, 1), # B, 32, 32, 32
nn.ReLU(True),
nn.Conv2d(32, 32, 4, 2, 1), # B, 32, 16, 16
nn.ReLU(True),
nn.Conv2d(32, 64, 4, 2, 1), # B, 64, 8, 8
nn.ReLU(True),
nn.Conv2d(64, 64, 4, 2, 1), # B, 64, 4, 4
nn.ReLU(True),
nn.Conv2d(64, 256, 4, 1), # B, 256, 1, 1
nn.ReLU(True),
View((-1, 256*1*1)), # B, 256
nn.Linear(256, z_dim*2), # B, z_dim*2
)
self.decoder = nn.Sequential(
nn.Linear(z_dim, 256), # B, 256
View((-1, 256, 1, 1)), # B, 256, 1, 1
nn.ReLU(True),
nn.ConvTranspose2d(256, 64, 4), # B, 64, 4, 4
nn.ReLU(True),
nn.ConvTranspose2d(64, 64, 4, 2, 1), # B, 64, 8, 8
nn.ReLU(True),
nn.ConvTranspose2d(64, 32, 4, 2, 1), # B, 32, 16, 16
nn.ReLU(True),
nn.ConvTranspose2d(32, 32, 4, 2, 1), # B, 32, 32, 32
nn.ReLU(True),
nn.ConvTranspose2d(32, 32, 4, 2, 1), # B, 32, 64, 64
nn.ReLU(True),
nn.ConvTranspose2d(32, nc, 4, 2, 1), # B, nc, 128, 128
)
self.classifier = nn.Sequential(nn.Linear(z_dim, 200))
self.weight_init()
self.classification_mode = False
def weight_init(self):
for block in self._modules:
for m in self._modules[block]:
kaiming_init(m)
def forward(self, x):
distributions = self._encode(x)
mu = distributions[:, :self.z_dim]
logvar = distributions[:, self.z_dim:]
z = reparametrize(mu, logvar)
x_recon = self._decode(z)
y = self.classifier(mu)
if self.classification_mode:
return y
else:
return y, x_recon, mu, logvar
def _encode(self, x):
return self.encoder(x)
def _decode(self, z):
return self.decoder(z)
def set_classification_mode(self, mode):
self.classification_mode = mode
class BetaVAE_H_CLASSIFIER(BetaVAE_H):
def __init__(self, vae_checkpoint_path, z_dim=10, nc=3, device='cuda:0'):
super(BetaVAE_H_CLASSIFIER, self).__init__(z_dim=z_dim, nc=nc)
if os.path.isfile(vae_checkpoint_path):
checkpoint = torch.load(vae_checkpoint_path, map_location=device)
self.load_state_dict(checkpoint['weights'])
else:
raise ValueError('Checkpoint Not Found: {}'.format(vae_checkpoint_path))
self.classifier = nn.Linear(z_dim, 200)
def forward(self, x):
with torch.no_grad():
encoded_x = self._encode(x)
encoded_x = encoded_x[:, :self.z_dim] # take the mean
return self.classifier(encoded_x)
def create_betavae(z_dim):
def initialize_betavae():
return BetaVAE_H(z_dim=z_dim, nc=3)
return initialize_betavae
def create_betavae_classifier(vae_checkpoint_path, z_dim, device):
def initialize_betavae_classifier():
return BetaVAE_H_CLASSIFIER(vae_checkpoint_path, z_dim=z_dim, nc=3, device=device)
return initialize_betavae_classifier